Processing and visualization of textual data based on syntactic dependency trees and sentiment scoring

ABSTRACT

Described herein are improved systems and methods for overcoming technical problems associated with processing and visualization of textual data and natural language processing. In some examples, a method is provided for determining sentiment associated with big data analysis of database information. In some examples, textual news data (e.g., NEWS API, RSS, etc.) is received via a communications network from a plurality of data platforms. The textual news data is parsed, and syntactic dependency trees are generated therefrom. A sentiment score is derived for the parsed textual data corresponding to a word or phrase associated with the textual data, and an image is generated reflecting scored sentiment for the parsed textual data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. Non-Provisional patentapplication Ser. No. 18/177,711 filed on Mar. 2, 2023, and entitledPROCESSING AND VISUALIZATION OF TEXTUAL DATA BASED ON SYNTACTICDEPENDENCY TREES AND SENTIMENT SCORING, the entire disclosure of whichapplication is hereby incorporated herein by reference.

U.S. Non-Provisional patent application Ser. No. 18/177,711 claims thebenefit of priority from U.S. Provisional Patent Application No.63/316,361 filed on Mar. 3, 2022, and entitled NATURAL LANGUAGE PROGRAMPRODUCT BASED ON SENTIMENT EXTRACTION, the entire disclosure of whichapplication is hereby incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to processing and visualization oftextual data and natural language processing.

BACKGROUND

The processing of textual data and natural language processing (NLP) mayhave started with the work of Alan Turing in the 1950s. Alan Turing isoften associated with the Turing test, a test of a machine's ability toexhibit intelligent behavior equivalent to a human. The test includesthe evaluation of natural language conversations between a human and amachine designed to generate human-like responses. For the machine toperform such a function, NLP became necessary and a child of theinformation age. Currently, NLP is an evolving technology related toartificial intelligence (AI), and the research and development of NLPfall within the greater disciplines of computer science and computerengineering.

SUMMARY

Described herein are improved systems and methods for overcomingtechnical problems associated with the processing and visualizingtextual data and natural language processing. In some examples, a methodis provided for determining sentiment associated with big data analysisof database information. In some examples, textual news data (e.g., NEWSAPI, RSS, etc.) is received via a communications network from aplurality of data platforms. The textual news data is parsed, andsyntactic dependency trees are generated therefrom. A sentiment score isderived for the parsed textual data corresponding to a word or phraseassociated with the textual data, and an image is generated reflectingscored sentiment for the parsed textual data.

Although many of the examples described herein pertain to the processingand visualization of textual news data, it is to be understood that thetechniques described herein can be applied to any type of textual data,and the disclosure is not limited to news data use cases.

In summary, the systems and methods (or techniques) disclosed herein canprovide specific technical solutions to at least overcome the technicalproblems mentioned in the application and other technical problems notdescribed herein but recognized by those skilled in the art.

With respect to some embodiments, disclosed herein are computerizedmethods for processing and visualization of textual data, naturallanguage processing, and a non-transitory computer-readable storagemedium for carrying out technical operations of the computerizedmethods. The non-transitory computer-readable storage medium hastangibly stored thereon, or tangibly encoded thereon, computer-readableinstructions that, when executed by one or more devices (e.g., one ormore personal computers or servers), cause at least one processor toperform a method for a novel and improved processing and visualizationof textual data as well as natural language processing.

With respect to some embodiments, a system is provided that includes atleast one computing device configured to provide useful and novelprocessing and visualization of textual data as well as natural languageprocessing. And with respect to some embodiments, a method is providedto be performed by at least one computing device. In some exampleembodiments, computer program code can be executed by at least oneprocessor of one or more computing devices to implement functionality inaccordance with at least some embodiments described herein, and thecomputer program code being at least a part of or stored in anon-transitory computer-readable medium.

For example, some embodiments include a method including receiving, by acomputing system, textual news data via a communications network from aplurality of data sources as well as parsing, by the computing system,the received textual news data. The method also includes generating, bythe computing system, a plurality of syntactic dependency treesaccording to the parsed textual news data. And the method includesdetermining, by the computing system, a sentiment score for the parsedtextual news data corresponding to a word or phrase associated with theparsed textual news data according to the generated plurality ofsyntactic dependency trees. Also, the method includes generating, by thecomputing system, an image including the determined sentiment score anddisplaying, via a graphical user interface (GUI), the determinedsentiment score. In some embodiments, the method includes statisticalsampling, by the computing system, the received textual news data, andthe determination of sentiment score is further based on statisticalsampling of the received textual news data.

As an example alternative, some embodiments include a method includingreceiving, by a computing system, textual news data via a communicationsnetwork from a plurality of data sources as well as constructing, by thecomputing system, a message including the received textual news data.The method continues with parsing, by the computing system, theconstructed message, generating, by the computing system, a plurality ofsyntactic dependency trees according to the parsed message, anddetermining, by the computing system, a sentiment score for the parsedmessage corresponding to a word or phrase associated with the parsedmessage according to the generated plurality of syntactic dependencytrees. Also, the method includes generating, by the computing system, animage including the determined sentiment score and displaying, via aGUI, the determined sentiment score.

These and other important aspects of the invention are described morefully in the detailed description below. The invention is not limited tothe particular assemblies, apparatuses, methods, and systems describedherein. Other embodiments can be used, and changes to the describedembodiments can be made without departing from the scope of the claimsthat follow the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the disclosure. Embodiments of the invention will now bedescribed, by way of example, with reference to the accompanyingdrawings listed in this Brief Description of the Drawings.

FIG. 1 illustrates an example network of computing systems to implementtechnologies for the systems and methods described herein, as well asforeseeable derivatives thereof, in accordance with some embodiments ofthe present disclosure.

FIG. 2 is a block diagram of example aspects of an example computingsystem, in accordance with some embodiments of the present disclosure.

FIGS. 3 to 9 illustrate example methods, in accordance with someembodiments of the present disclosure.

FIG. 10 is a chart showing various narrative arc examples, in accordancewith some embodiments of the present disclosure;

FIG. 11 illustrates a chart showing AI as an intended audience forpublications, in accordance with some embodiments of the presentdisclosure.

FIG. 12 illustrates a graphical representation of sentiment determinedfor certain words or phrases, in accordance with some embodiments of thepresent disclosure.

FIG. 13 illustrates another graphical representation of sentimentdetermined for certain words or phrases, in accordance with someembodiments of the present disclosure.

FIG. 14 illustrates a horizontal bar graph representing the sentiment ofwords found in news sources, in accordance with some embodiments of thepresent disclosure.

FIG. 15 illustrates a chart showing a sentiment depiction for threecandidate pitches/messages under consideration for use by a fictitiousgreen utility, in accordance with some embodiments of the presentdisclosure.

FIG. 16 illustrates a program screen for an NLP program showing asentiment analyzer for phrases that can be used, for instance, in acompany pitch or message, in accordance with some embodiments of thepresent disclosure.

FIG. 17 illustrates a chart showing the functional process of the NLPaccording to this disclosure, in accordance with some embodiments of thepresent disclosure.

FIG. 18 illustrates a chart showing additional functional processingthat can be fueled beyond that shown in FIG. 17 , in accordance withsome embodiments of the present disclosure.

FIG. 19 illustrates a sentiment diagram showing an example of sentimentmapping concerning a data set, in accordance with some embodiments ofthe present disclosure.

FIG. 20 is a graph showing a different aspect of the data from FIG. 10 ,in accordance with some embodiments of the present disclosure.

FIG. 21 shows a plot of trend lines concerning the data set used withFIGS. 10 and 11 , in accordance with some embodiments of the presentdisclosure.

FIG. 22 is a chart showing data characterizations, in accordance withsome embodiments of the present disclosure.

FIG. 23 is a horizontal bar graph displaying sentiment related to aparticular party, in accordance with some embodiments of the presentdisclosure.

FIG. 24 illustrates a screenshot of a visualizer wherein data can besorted dynamically, in accordance with some embodiments of the presentdisclosure.

FIG. 25 shows a different screenshot of the visualizer, characterizing aconversation as poor, in accordance with some embodiments of the presentdisclosure.

FIG. 26 illustrates a plot of malign actors involved in news coverage,in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION

Described herein are improved systems and methods for overcomingtechnical problems associated with processing and visualization oftextual data and natural language processing. In some examples, a methodis provided for determining sentiment associated with big data analysisof database information. In some examples, textual news data (e.g., NEWSAPI, RSS, etc.) is received via a communications network from aplurality of data platforms. The textual news data is parsed, andsyntactic dependency trees are generated therefrom. A sentiment score isderived for the parsed textual data corresponding to a word or phraseassociated with the textual data, and an image is generated reflectingscored sentiment for the parsed textual data. Although many of theexamples described herein pertain to the processing and visualization oftextual news data, it is to be understood that the techniques describedherein can be applied to any type of textual data, and the disclosure isnot limited to news data use cases.

With respect to some embodiments disclosed herein, this applicationpertains to textual media content based on examination and analysis of acorpus of literature using machine learning to provide a basis fordetermining sentiment, over time, with the purpose of identifying andpossibly changing media narratives, e.g., graphical representations oftrends.

It has been theorized that a correlation exists between sentiment andcontemporaneously published literature and articles. Hundreds ofthousands of written works or more can be analyzed via AI describedherein, and the repeated input of such works can improve the AI throughmachine learning. The AI can determine article sentiment over time, asgleaned from contemporary media, along with the discovery of narrativetrends, demonstrable on a coordinate axis, reflective of positive andnegative sentiment as registered by valence and arousal associated withwords or groups of words. In some embodiments, the words or groups ofwords are e-chunks, each being an individual word or a group of wordsfound in a body of text that correlates to a distinct unit of sentiment.

FIG. 1 illustrates an example network of computing system 100 toimplement technologies for the systems and methods described herein, aswell as foreseeable derivatives thereof, in accordance with someembodiments of the present disclosure. For example, the network ofcomputing system 100 can implement any of the aforesaid and proceedingcomponents and operations described herein. The network of computingsystem 100 is shown, including a backend computing component 102 and afrontend computing component 103. As shown in FIG. 1 , the backendcomputing component 102 can be hosted on server computers (e.g., seeserver devices 108 and 110). Also, as shown in FIG. 1 , the frontendcomputing component can be hosted on client computers (e.g., see clientdevices 112 a, 112 b, and 112 c). The network of computing system 100 isalso shown, including one or more LAN/WAN networks 114 which are showncommunicatively coupling the server computers and the client computers.The LAN/WAN network(s) 114 can include one or more local area networks(LAN(s)) and/or one or more wide area networks (WAN(s)). The LAN/WANnetwork(s) 114 can include the Internet and/or any other interconnectedcommunications network. The LAN/WAN network(s) 114 can also include asingle computer network or a telecommunications network. Morespecifically, the LAN/WAN network(s) 114 can include a local areanetwork (LAN) such as a private computer network that connects computersin small physical areas, a wide area network (WAN) to connect computerslocated in different geographical locations, and/or a metropolitan areanetwork (MAN)—also known as a middle area network—to connect computersin a geographic area larger than that covered by a large LAN but smallerthan the area covered by a WAN.

At least each shown component of the network of computing system 100 canbe or include a computing system which can include memory that caninclude media. The media can include or be volatile memory components,non-volatile memory components, or a combination of such. In general,each of the computing systems can include a host system that uses thememory. For example, the host system can write data to the memory andread data from the memory. The host system can be a computing devicesuch as a desktop computer, laptop computer, network server, mobiledevice, or such computing device that includes a memory and a processingdevice. The host system can include or be coupled to the memory so thatthe host system can read data from or write data to the memory. The hostsystem can be coupled to the memory via a physical host interface. Thephysical host interface can provide an interface for passing control,address, data, and other signals between the memory and the host system.

FIG. 2 is a block diagram of example aspects of an example computingsystem 200, in accordance with some embodiments of the presentdisclosure. FIG. 2 illustrates parts of the computing system 200 withinwhich a set of instructions for causing the machine to perform any ofthe methodologies discussed herein can be executed. In some embodiments,the computing system 200 can correspond to a host system that includes,is coupled to, or utilizes memory or can be used to perform theoperations of a controller (e.g., to execute an operating system toperform operations corresponding to the backend computing component 102or the frontend computing component 103). In alternative embodiments,the machine can be connected (e.g., networked) to other machines in aLAN, an intranet, an extranet, and/or the Internet. The machine canoperate in the capacity of a server or a client machine in aclient-server network environment, as a peer machine in a peer-to-peer(or distributed) network environment, or as a server or a client machinein a cloud computing infrastructure or environment.

The machine can be a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a server, a network router, a switch or bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while a single machine is illustrated, the term “machine” shall also betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein.

The example computing system 200 includes a processing device 202, amain memory 204 (e.g., read-only memory (ROM), flash memory, dynamicrandom-access memory (DRAM), etc.), a static memory 1206 (e.g., flashmemory, static random-access memory (SRAM), etc.), and a data storagesystem 210, which communicate with each other via a bus 230.

The processing device 202 represents one or more general-purposeprocessing devices such as a microprocessor, a central processing unit,or the like. More particularly, the processing device can be amicroprocessor or a processor implementing other instruction sets or acombination of instruction sets. The processing device 202 can also beone or more special-purpose processing devices such as an applicationspecific integrated circuit (ASIC), a field programmable gate array(FPGA), a digital signal processor (DSP), a network processor, or thelike. The processing device 202 is configured to execute instructions214 for performing the operations discussed herein. The computing system200 can further include a network interface device 208 to communicateover the LAN/WAN network(s) 114 of FIG. 1 .

The data storage system 210 can include a machine-readable storagemedium 212 (also known as a computer-readable medium) on which is storedone or more sets of instructions 214 or software embodying any one ormore of the methodologies or functions described herein. Theinstructions 214 can also reside, completely or at least partially,within the main memory 204 and/or within the processing device 202during execution thereof by the computing system 200, the main memory204, and the processing device 202, also constituting machine-readablestorage media.

In one embodiment, the instructions 214 include instructions toimplement functionality corresponding to the backend computing component102 or the frontend computing component 103. While the machine-readablestorage medium 212 is shown in an example embodiment to be a singlemedium, the term “machine-readable storage medium” should be taken toinclude a single medium or multiple media that store the one or moresets of instructions. The term “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing orencoding a set of instructions for execution by the machine and thatcauses the machine to perform any one or more of the methodologies ofthe present disclosure. The term “machine-readable storage medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

FIGS. 3 to 9 illustrate example operations of the computing systemsdescribed herein (e.g., see computing system 200), in accordance withsome embodiments of the present disclosure. FIGS. 3 to 9 illustratemethods 300 to 900, respectively.

The method 300 commences, at step 302, with receiving, by a computingsystem, textual news data via a communications network from a pluralityof data sources. At step 304, the method 300 includes parsing, by thecomputing system, the received textual news data (e.g., the data parsedby the backend computing component 102 shown in FIG. 1 ). At step 306,the method 300 includes generating, by the computing system, a pluralityof syntactic dependency trees according to the parsed textual news data(e.g., the trees generated by the backend computing component 102).Also, the method 300, at step 314, includes determining, by thecomputing system, a sentiment score for the parsed textual news datacorresponding to a word or phrase associated with the parsed textualnews data according to the generated plurality of syntactic dependencytrees (e.g., the score determined by the backend computing component102). In some embodiments, including embodiments of the method 300, theparsed data, a part of the parsed data, the corresponding word, or thecorresponding phrase is or includes an e-chunk or becomes an e-chunkafter an iteration of the method.

At step 316, the method 300 includes generating, by the computingsystem, an image including the determined sentiment score (such as thefrontend computing component 103 shown in FIG. 1 generating the image).And at step 318, the method 300 includes displaying, via a GUI (such asa GUI rendered by the frontend computing component 103), the determinedsentiment score.

As shown in FIG. 3 , the method 300 also includes, at step 307,statistical sampling, by the computing system, the received textual newsdata (e.g., the sampling being performed by the backend computingcomponent 102). And the determination of the sentiment score, at step314, is further based on statistical sampling of the received textualnews data.

Further, as shown in FIG. 3 , the method 300 includes, at step 308,resolving, by the computing system, syntactic ambiguity in each one ofthe generated plurality of syntactic dependency trees prior todetermining the sentiment score (e.g., the ambiguity resolved by thebackend computing component 102). And the determination of the sentimentscore is based on the resolved plurality of syntactic dependency trees.

Also, as shown in FIG. 3 , the method 300 includes determining, by thecomputing system, a respective accuracy score for each one of theresolved plurality of syntactic dependency trees (at step 310). In someexamples, the determination of the scores is by the backend computingcomponent 102. The assigning an accuracy score to a syntactic dependencytree of the resolved plurality of syntactic dependency trees is based onan estimation of accuracy in resolving syntactic ambiguity in thesyntactic dependency tree. As shown, the assignment of the respectiveaccuracy scores occurs prior to determining the sentiment score, and thedetermination of the sentiment score is further based on the determinedaccuracy scores for each one of the resolved plurality of syntacticdependency trees.

Further, as shown in FIG. 3 , the method 300 includes selecting, by thecomputing system, a dependency tree of the resolved plurality ofsyntactic dependency trees that has the highest determined accuracyscore of the determined accuracy scores (at step 312). In someembodiments, such a selection is performed by the backend computingcomponent 102. And the determining the sentiment score according to theselected syntactic dependency tree having the highest determinedaccuracy score.

In some embodiments, such as shown by method 400 of FIG. 4 , the method300 is part of an application framework and a greater method. In suchembodiments, the application framework provides searchable content to besearched via the GUI. As shown in FIG. 4 , the method 400 includes allthe steps of method 300 and further includes deriving, by the computingsystem, the searchable content from the parsed textual news data (atstep 402). Also, in such examples, the method 400 further includesproviding, via the GUI, search functions that can search the searchablecontent using the generated plurality of syntactic dependency trees, thedetermined sentiment score, the determined accuracy scores, or acombination thereof as a basis for queries (at step 404).

In some embodiments of method 400, the search functions are part of auser search and audit tool interface provided by the GUI, and the searchfunctions further include queries that are definable by a useridentifier, a date, positive sentiment, negative sentiment, newsattribution, or any combination thereof.

In some embodiments of method 300 or 400 or other methods describedherein, the plurality of data sources includes application programminginterfaces (e.g., NEWS API), really simple syndication (RSS) feeds,social media platforms, news aggregators, social news aggregators (e.g.,REDDIT), or any combination thereof.

In some embodiments, such as shown in FIG. 5 , the method 300 is part ofa greater method, such as method 500. Method 500 includes identifying,by the computing system, the sentiment of or related to at least a partof the received textual news data based on a source of the at least partof the received textual news data (at step 502). Method 500 alsoincludes displaying, via the GUI, the sentiment based on the source ofthe at least part of the received textual news data (at step 504).

In some embodiments, such as shown in FIG. 6 , the method 300 is part ofa greater method, such as method 600. Method 600 includes identifying,by the computing system, the sentiment of or related to at least a partof the received textual news data based on a publication date of the atleast part of the received textual news data (at step 602). Method 600also includes displaying, via the GUI, the sentiment based on thepublication date of at least part of the received textual news data (atstep 604).

As shown in FIG. 7 , the method 700 commences, at step 702, withreceiving, by a computing system, textual news data via a communicationsnetwork from a plurality of data sources. At step 703, the method 700continues with constructing, by the computing system, a messageincluding the received textual news data. At step 704, the method 700includes parsing, by the computing system, the constructed message. Atstep 706, the method continues with generating, by the computing system,a plurality of syntactic dependency trees according to the parsedmessage. At step 707, the method includes statical sampling, by thecomputing system, the received textual news data.

Also, the method 700 includes, at step 714, determining, by thecomputing system, a sentiment score for the parsed message correspondingto a word or phrase associated with the parsed message according to thegenerated plurality of syntactic dependency trees. The method 700 alsoincludes generating, by the computing system, an image including thedetermined sentiment score (at step 716). And the method 700 includesdisplaying, via a GUI (such as a GUI render by frontend computingcomponent 103), the determined sentiment score (at step 718).

At step 708, the method 700 includes resolving, by the computing system,syntactic ambiguity in each one of the generated plurality of syntacticdependency trees prior to determining the sentiment score. And thedetermination of the sentiment score is based on the resolved pluralityof syntactic dependency trees. Also, at step 710, the method 700includes determining, by the computing system, a respective accuracyscore for each one of the resolved plurality of syntactic dependencytrees. In some such embodiments, the assigning an accuracy score to asyntactic dependency tree of the resolved plurality of syntacticdependency trees is based on an estimation of accuracy in resolvingsyntactic ambiguity in the syntactic dependency tree. And for example,the assignment of the respective accuracy scores occurs prior todetermining the sentiment score, and the determination of the sentimentscore is further based on the determined accuracy scores for each one ofthe resolved plurality of syntactic dependency trees. At step 712, themethod 700 includes selecting a dependency tree of the resolvedplurality of syntactic dependency trees that has the highest determinedaccuracy score of the determined accuracy scores. And the determining ofthe sentiment score is according to the selected syntactic dependencytree having the highest determined accuracy score.

As shown in FIG. 8 , the method 800 depends on method 700 and repeatsstep 714 (determining the sentiment score) until a determined sentimentscore of a preselected value is reached. At step 802, the method 800includes iteratively changing, by the computing system, the constructedmessage to produce a new message. And at step 804, the method 800includes determining a corresponding sentiment score for the new messageuntil a target sentiment score is reached—by repeating step 714 ofmethod 700.

Referring back to method 700, in some examples, the method is part of anapplication framework; the application framework provides searchablecontent to be searched via the GUI, and the method further includesderiving the searchable content from the parsed message. In suchexamples and others, the method can further include providing, via theGUI, search functions using the generated plurality of syntacticdependency trees, the determined sentiment score, the determinedaccuracy scores, or a combination thereof as a basis for queries. Insome examples, the search functions are part of a user search and audittool interface provided by the GUI, and the search functions furtherinclude queries that are definable by a user identifier, a date,positive sentiment, negative sentiment, news attribution, or anycombination thereof.

In some embodiments of method 700, the plurality of data sourcesincludes application programming interfaces (e.g., NEWS API), reallysimple syndication (RSS) feeds, social media platforms, newsaggregators, social news aggregators (e.g., REDDIT), or any combinationthereof. And in some examples, the method 700 includes displaying, viathe GUI, the sentiment of or related to at least a part of the receivedtextual news data based on a source of the at least part of the textualnews data. Also, the method 700 can include displaying, via the GUI, thesentiment of or related to at least a part of the received textual newsdata based on a date of publication of the at least part of the textualnews data. In some cases, the determination of the sentiment score isfurther based on statistical sampling of the received textual news data.

FIG. 9 shows another alternative example, method 900 of analyzing textto determine sentiment. The method 900 commences with receiving, by acomputing system, a first query input (at step 902), wherein the firstquery input includes text and wherein the text includes a first string.The method 900 also includes retrieving, by the computing system, asecond string associated or linked with the first string (at step 904).At step 906, the method 900 continues with conducting, by the computingsystem, tokenization analysis on the second string to identify, based onthe tokenization analysis, a minimum excerpt of the second string thatcontains discernable sentiment. In some embodiments, the tokenizationanalysis includes or occurs after normalizing a group of stringsassociated with and including the first and second strings. In someembodiments, the minimum excerpt is or includes an e-chunk. For thepurposes of this disclosure, it is to be understood that an e-chunk isan individual word or a group of words found in a body of text thatcorrelates to a distinct unit of sentiment.

At step 908, the method 900 continues with storing, by the computingsystem, the query input, the first string, the second string, and theminimum excerpt in a database to be used to analyze another query inputor to analyze the query input to improve analysis of the query inputiteratively. As shown, method 900 includes a repeatable loop (see arrow910) in that the other input or the input can be, include, or be a partof the first query input of step 902. In this way, the overall method900 can be enhanced, or its output can be enhanced with each iterationof the method. In this sense, the method includes machine learning.Also, additional machine learning techniques can be utilized to improvemethod 900 and at least parts of the other methods described herein.

In some examples of the method 900, the method further includesanalyzing the minimum excerpt within the context of the query input torefine the discernable sentiment. In some examples of the method 900,the method further includes assigning a sentiment score to the minimumexcerpt based on the refined discernable sentiment. In some examples ofthe method 900, the sentiment score includes a range of sentiments. Insome such examples, the sentiment score includes a range from negativesentiment to positive sentiment. In some examples of the method 900, thedatabase includes a database of contextually analyzed andsentiment-scored text. And in some examples of the method 900, themethod further includes visually representing the sentiment score on aGUI. Also, the sentiment score can be provided in a GUI with sentimentscores of other strings, such as being provided in a graph format.

FIG. 10 is a chart showing a variety of narrative arc examples, such asrags to riches (a rising graph); man-in-a-hole (a falling graph followedby a rising graph); Cinderella (a rising graph followed by a fallinggraph, followed by a rising graph); rags-to-riches (a falling graph);Oedipus (a rising graph followed by a falling graph); and Icarus (afalling graph followed by a rising graph, followed by a falling graph).Contemporary written news media was fed into an example of NLP used bysome embodiments described herein to score words based on sentiment. Forexample, step 304 of method 300, shown in FIG. 3 , can include at leasta part of such NLP to parse the received textual news data prior togenerating syntactic dependency trees in step 306 of the same method.Also, in some embodiments, different parts of the NLP can be used in twoor more of the steps 304, 306, 307, 308, 310, 312, and 314 of method300. Also, one or more aspects of the methods 400 to 900 can use one ormore parts of the NLP. It is to be understood that the example NLP caninclude any know techniques for natural language processing. Also, it isunderstood that the methods described herein can be implemented by asystem (such as any one or more of the computing systems of FIG. 1 orcomputing system 200 shown in FIG. 2 ).

In FIG. 10 , the combination of valence and arousal, referred to assentiment, of words was determined and depicted on a Cartesiancoordinate graph. The Cartesian coordinate graph provides a view ofnegative and/or positive sentiments over time and the degree ofpositivity or negativity. The research sought to find how contemporarynews media narratives follow predictable narrative arcs. This conceptcan be applied to corporate content regarding how news coverage canaffect a given company. A distinct set of trends were envisioned basedon the aggregate sentiment of the news. Consequently, a question to bedetermined was whether or not a discernible signal could be found in agiven narrative (e.g., any kind of news coverage) by using an extensivedata review of the news. This amounted to asking robots to read thenews.

An NLP test was applied to Internet articles concerning Donald Trump andhis campaign for President in 2016. The test aimed to determine whatkind of signals (i.e., what kind of discernible narrative arc) could beobserved concerning sentiment. Initial results contained a series ofsolid signals, but the discernible narrative arcs posited by Vonnegutwere not observed. Nonetheless, the other signals could be sorted basedon a series of variables, such as volume over time, publication writer,or the type of article. This opened up a range of possibilities. Themapping correlation presented some options for determining sentiment inthe news and its correlation with various parameters such as stockprice.

Consequently, using an application programming interface (API), datafrom datasets of several types were fed to a processor. The initialvisualizations of the signals within the datasets were performed using avisual mapping process. The visual mapping process can includegeocoding, in which the process can automatically transform locationdata and associated information into interactive visual maps, such asvisual maps that can be zoomed in and out of. The mapping process caninclude functions that can process census-based population, income, andother standard demographic datasets and can also be used to processdatasets associated with words and phrases. In the visual environment ofthe mapping process, users can see aspects of the data sets visually andshare what is reviewed through the maps generated by the process. Insome embodiments, the mapping process can occur or include TABLEAU(https://www.tableau.com/solutions/maps).

With such a mapping process or other types of data analysis, aninterrogatable signal can be pulled from data noise by the system. Thisincreased data availability, thereby allowing searches on various topicsover any given time (such as searches performed by the system). Themachine learning and NLP of the system can be refined by rewritingcertain aspects of instructions for the system (e.g., see instructions214 shown in FIG. 2 ) to improve targeting, tokenization, normalization,and other rules to determine the relationship between words, etc. Thesystem further refined word associations with negations in sentences toassess sentiment. A narrative sentiment over time, overlaid with thestock price, proved insufficiently interrogable to produce a sentimentmapping that was strongly correlated with the stock price. However, itwas found that sentiment arcs could be applied, by the system, tocompanies and topics, themes, and personalities, and it could bedetermined, by the system, the types of sentiment-laden words used inconnection with publications online about such subjects. Discerniblesentiment arcs were pulled, by the system, from datasets with at leastsome regularity dispensing with the need to rebuild data for sentimentanalysis each program run. This permitted sentiment searches whichprovided a further basis for analysis. A platform of the system wasdeveloped to take language found on the Internet and predictivelydetermine sentiment using a visual representation. For instance, theplatform can algorithmically score the language of a sentence using therelationship between words. It was discovered that machines wereincreasingly the intended audience for many kinds of content found onthe web, whether public relations (PR), securities filings, etc., andthe knowledge of this can be used by the system.

FIG. 11 illustrates a chart that shows AI as an intended audience forpublications. In just over a decade, machine-generated downloads ofcorporate 10-K and 10-Q filings have increased significantly. Forinstance, it was hypothesized that social media was, in fact, no longerexclusively intended for a human audience but rather, increasingly, fora robot (bot) audience to a majority extent. Much of the news receivedby the system is edited, sorted, and prioritized by AI in connectionwith using NLP in an NLP environment. Consequently, companies that wishto accomplish the desired outcome of communication and engagement withstakeholders should adjust how they talk about their finances, brands,and forecasts in the age of AI. With the application of the system to agiven business, it is possible to sort information that will yieldresults beyond whether a given article is simply good or bad, happy orsad, or positive or negative. Specifically, a platform or program of thesystem can determine and assign, via its instructions, phrase sentimentvalues within the context of finding and sorting the frequency of wordsand terms being used in or for an online publication during a givenperiod. The platform or program aims to provide the capability for agiven company to understand how AI sees that company's business andbrand. This includes determining how AI sorts and prioritizes businessinformation. This capability is demonstrated by FIGS. 12 and 13 , whichillustrate displays produced by the NLP of a graphical representation ofsentiment determined for certain words or phrases.

The sentiment in FIG. 12 pertains to the phrase “lab leak,” a topic inthe news concerning the SARS-CoV-2 coronavirus. The NLP of the systemscours the news for signals (e.g., words or phrases for which detectionis sought in news resources such as online publications). The frequencyof use of certain detected words or phrases (such as “lab leak” as shownin FIG. 12 or Elon Musk in FIG. 13 ) is noted by the ordinate (y-axis)as the distinct number of articles (article count). Further, each one ofthe stacked bars shown in FIGS. 3 and 4 represent a news article from apublication wherein words can be sorted and colors grouped and assignedbased on whether the usage of words therein is positive (green), neutral(yellow), or negative (red or reddish), representing the sentimentbehind the usage of those words.

Additionally, shading the color towards a more negative sentiment usinga more reddish hue allows further distinction of the sentiment of adetected word or phrase in an article. Also, a more positive sentimentcan be expressed using a greener hue. The article date is indicated onthe abscissa (or x-coordinate). For instance, as shown in FIG. 12 , eachnews article containing the detected phrase “lab leak” is accounted forby a sentiment color plotted on the publication date of the newsarticle. Each vertical bar of color shows the number of pertinentarticles mentioning the phrase “lab leak,” and each article is groupedby common color.

Consequently, for instance, should ten articles contain the phrase “lableak” on a particular date, e.g., Mar. 1, 20xx, then a multi-color barshowing the number of articles of the same sentiment grouped isrepresented on the graph. Therefore, should ten articles reflect thedetected phrase “lab leak” with two articles of positive sentiment,three articles of neutral sentiment, and five articles of negativesentiment, then a bar will be shown, on the article date, having colorsreflecting the respective proportion of sentiments. For this example,that means one-fifth of the bar will be green, three-tenths of the barwill be yellow, and one-half of the bar will be red. The bars displaypositive, neutral, and negative articles in that order, starting fromthe abscissa (or the x-axis). Regarding FIG. 12 , most of the negativesentiment associated with “lab leak” occurred in July 2021.

Generally, after a word or phrase is chosen as a target, a processorrunning the NLP searches for the chosen word or phrase among manysources over the Internet. The NLP processor then matches the phrase tothe text from an Internet source. The NLP parses the one or more longerphrases or sentences in which the phrase is found. For instance, theword to be searched for can be “water.” “Water,” as a word withoutcontext, can be considered neutral in sentiment. However, placing theadjective “scalding” before it, as in “scalding water,” will likelycarry negative connotations and, therefore, negative sentiment.Likewise, the adjective “freezing” placed before “water” also likelycarries a negative implication and, hence, negative sentiment. In fact,“freezing” or “scalding” placed within four of five words of water canbe regarded as having a negative connotation and, therefore, a negativesentiment. By contrast, “spa” as used with “water” or “drinking” placedbefore “water” can likely carry positive implications and positivesentiment. Further, “spa” or “drinking” used within four or five wordsof “water” can have a positive connotation and, therefore, a positivesentiment.

The NLP of the system can also match sentiment concerning a noun withits use with certain words such as “good, “bad,” or “evil” appearing ina sentence or phrase nearby. Further, whether positive, negative, bad,or good, connotations can be drawn in connection with parsing textualdata. After that, the NLP extracts syntactic dependency trees from thattextual data. This extraction is referred to as “syntactic dependencyparsing.” Syntactic dependency parsing returns various dependence parsetags describing the relationship between two words in a sentence.Syntactic dependency trees are extracted to recognize a sentence andassign a syntactic structure. This is accomplished in connection with aparse tree generated by a parsing algorithm. A given sentence cangenerate many parse trees, commonly called “ambiguities.” The NLP uses asyntactic disambiguation algorithm to select the most accurate parsetree.

FIG. 14 illustrates an NLP display of the system representing anothersentiment of words found in news sources portrayed on a horizontal bargraph. Specific word phrases are shown along the ordinate, and thenumber of times the word or phrase occurs is shown along the abscissa.For a given advertising pitch, the NLP, according to this disclosure,can score the sentiment of phrases/words under consideration for use.

FIG. 15 illustrates a chart of the system showing a sentiment depictionfor three candidate pitches/messages under consideration for using“ExampleCorp,” a fictitious green utility with messaging concerning thebenefits of its wind turbines or solar power program. The NLP helpsidentify words that drive positive sentiment and flags the words to beavoided. As such, a message can be proactively scored.

According to this disclosure, FIG. 16 illustrates a program display ofthe NLP of the system, showing a sentiment visualizer and analyzer ofthe system for phrases that can be used, for instance, in a companypitch or message. The sentiment visualizer and analyzer provides asyntactic mapping of phrases to identify, among other things, thelinguistic structure of a phrase, thereby shedding light on the semanticrole of words in a phrase. This sentiment analyzer enables interrogationof, for instance, news headlines, thereby enabling the readydetermination of why the sentiment algorithm gave a particular score towords/phrases. According to this disclosure, the NLP allows the entry ofa headline and diagrams (as a type of parse tree) of the relationshipsfound therein. As such, a diagram can specify, for instance, thatcontent functions as a noun, an adjective, a verb, or a word thatmodifies another word, etc. It can also allow input as to the target ofa sentence and show that not all words have equal value. Some words canbe shown to be sentiment-laden, while others can have relationships withone another.

The sentiment visualizer and analyzer can be used as a forward writingtool that permits content creation. Headlines can be placed in variousplaces, along with the use of various press releases. Further, thesentiment visualizer and analyzer determines sentiment changes based onchanging one word, for instance, in an advertising campaign, to another.Moreover, the sentiment visualizer and analyzer allows determination ofwhat happens should the order of words be rearranged, or moresentiment-laden words are employed. Further, the sentiment visualizerand analyzer can provide several syntactic mappings for a phrase. Inconnection with the NLP running a subroutine that employs an algorithmusing linguistic disambiguation to estimate the accuracy of eachsyntactic mapping, the syntactic mapping candidate with the highestestimation score is chosen as the correct syntactic mapping for thephrase. Such a candidate is shown in FIG. 16 .

According to the disclosure herein, the NLP assigns a sentimentvaluation to a chosen syntactic mapping by providing a sentiment scoreto a particular phrase/word as it appears in each use of the phrase/wordfound in the pool of data being analyzed. The display mapping, asdiscussed herein, results from these mappings and scoring.

FIG. 17 illustrates a chart showing the functional process of the NLPaccording to this disclosure. An audit is performed, and adjacenciesbetween companies are determined. The sentiment is examined, and alexicon is investigated. Words can be further interrogated, which allowsfor determining which words work and which have associations. For acompany that considers itself innovative, it can be determined whetherthat word (innovative) ever appears in news coverage.

Further, should “innovative” appear in the literature associated with acompany, a determination can be made as to whether the associatedsentiment is, for instance, extremely positive. The NLP can alsodetermine whether competitor news articles show similar sentiments, andcontent can be analyzed on an ongoing basis.

FIG. 18 illustrates a chart showing additional functional processing ofthe system that can be fueled beyond the functional process illustratedin FIG. 17 . This additional functional processing can include, forinstance, quantitative and qualitative research, website and platformdevelopment, content creation, etc.

FIG. 19 illustrates a sentiment display of the system showing an exampleof sentiment mapping concerning a data set for Company “Z” (Co. “Z”), afictitious company. This mapping is formed from about 100,000 articlesabout Co. “Z.” Each one of the bars represents a news article. The onesat the top (the most negative) are in red, and green bars representpositive articles about Co. “Z.” Yellow bars represent neutral articlesabout Co. “Z.”

FIG. 20 shows a different aspect of the data from FIG. 19 . As shown,data can be sorted in different ways, including by publisher. Forinstance, a determination can be made of sentiment shown in articles,from a corresponding dataset, in a news publication. According to thedisclosure, the NLP permits the scoring of the sentiment of articles in,for instance, the NEW YORK TIMES. Trend lines about the coverages,positive or negative, can also be determined.

FIG. 21 shows a plot of trend lines the system provides concerning thedata set used with FIGS. 3 and 4 . The trend lines indicate the extentof positive or negative coverage. The sentiment is expressed on theordinate (y-axis), over a range from +1 to −1, with neutral sentiment(0.0) occurring at the intersection of the ordinate with the abscissa(x-axis). Very positive sentiment (e.g., +1) on the graph is representedwith green, while very negative sentiment (e.g., −1) is represented onthe graph with red. More neutral sentiment appears in yellow on thegraph. The x-axis represents time. As shown in FIG. 21 , the time rangesover a period from January (Jan) 20 (2020) through October (Oct) 21(2021).

FIG. 22 is a chart the system provides, showing data characterizations;for instance, sentiment about Co. “Z” can be illustrated with a certainsentiment assigned thereto. In addition, the density of coverage of aparticular company over a period of time can be displayed (not shown).

FIG. 23 illustrates an NLP display of a horizontal bar graph provided bythe system, showing sentiment related to the CEO of PFIZER, AlbertBourla, showing the most commonly used words associated with Bourla in adataset. As one might expect, those words prominently feature “COVID-19”and “vaccine.” The data can be exported, by the system, to aspreadsheet, producing, for instance, six thousand to ten thousand cellslong. The system can then sort words, for instance, based on howpositive, negative, or often they occur.

FIG. 24 illustrates a screenshot of a sentiment visualizer and analyzerof the system (of the type discussed with respect to FIG. 16 ), whereindata can be sorted dynamically. For instance, the system can initiallyscore a conversation dynamically based on its characterizations as anexcellent conversation. However, as shown in FIG. 25 , which shows adifferent screenshot of the visualizer and analyzer, characterizing theconversation as poor, a different dynamic mapping of words can resultfrom identifying various modifiers of the word “poor” that relate to apoor conversation. Databases can be employed by the system, wheredictionary words have been scored. These words were sent to people, inconnection with a survey, who, for nominal compensation, scored wordsand phrases on a scale from negative one (−1) to a positive one (+1).one (+1) being more positive and negative one (−1) being more negativein perception. According to this disclosure, this data type was used toshape the sentiment rankings used with the NLP. Further, sentimentscoring, performed by the system, can be based upon a statisticalsampling of the population of sentiment responses from the survey.

The NLP of the system can permit a view of the performance of a languagefrom a lexicon. This can allow a focus on gaining insights into negativenarratives, for instance, about a company that may exist. The NLPprovides a basis to counteract negative narratives with positivenarratives.

FIG. 26 illustrates a plot, provided by the system, of malign actorsinvolved in news coverage. This provides a view of propaganda outletsand how they can seek to weaponize language. As shown in FIG. 26 ,sentiment over time for various news sources is reflected therein.Inflammatory content can be identified, by the system, that can drive aparticularly dangerous narrative.

It is to be understood that none of the steps described herein isessential or indispensable. Any steps can be adjusted or modified, andother or additional steps can be used. Any portion of any of the steps,processes, structures, and/or devices disclosed or illustrated in oneembodiment, flowchart, or example in this specification can be combinedor used with or instead of any other portion of any of the steps,processes, structures, and/or devices disclosed or illustrated in adifferent embodiment, flowchart, or example. The embodiments andexamples provided herein are not intended to be discrete and separatefrom each other.

The section headings and subheadings provided herein are non-limiting.The section headings and subheadings do not represent or limit the fullscope of the embodiments described in the sections to which the headingsand subheadings pertain. For example, a section titled “Topic 1” mayinclude embodiments that do not pertain to Topic 1, and embodimentsdescribed in other sections may apply to and be combined withembodiments described within the “Topic 1” section.

The features and processes described above may be used independently orcombined. All possible combinations and sub-combinations are intended tofall within the scope of this disclosure. In addition, certain methods,events, states, or process blocks may be omitted in someimplementations. The methods, steps, and processes described herein arealso not limited to any particular sequence, and the blocks, steps, orstates relating thereto can be performed in appropriate sequences. Forexample, described tasks or events may be performed in an order otherthan the order specifically disclosed. Multiple steps may be combined ina single block or state. The example tasks or events may be performed inserial, parallel, or another manner. Tasks or events may be added orremoved from the disclosed example embodiments. The example systems andcomponents described herein may be configured differently thandescribed. For example, elements may be added to, removed from, orrearranged compared to the disclosed example embodiments.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless expressly stated otherwise,or otherwise understood within the context as used, is generallyintended to convey that certain embodiments include, while otherembodiments do not include, certain features, elements and/or steps.Thus, such conditional language is not generally intended to imply thatfeatures, elements, and/or steps are in any way required for one or moreembodiments or that one or more embodiments necessarily include logicfor deciding, with or without author input or prompting, whether thesefeatures, elements and/or steps are included or are to be performed inany particular embodiment. The terms “comprising,” “including,”“having,” and the like are synonymous and are used inclusively, in anopen-ended fashion, and do not exclude additional elements, features,acts, operations, and so forth. Also, the term “or” is used in itsinclusive sense (and not in its exclusive sense) so that when used, forexample, to connect a list of elements, the term “or” means one, some,or all of the elements in the list. Conjunctive language such as thephrase “at least one of X, Y, and Z,” unless expressly stated otherwise,is otherwise understood with the context as used in general to conveythat an item, term, etc., may be either X, Y, or Z. Thus, suchconjunctive language is not generally intended to imply that certainembodiments require at least one of X, at least one of Y, and at leastone of Z to each be present.

The term “and/or” means that “and” applies to some embodiments, and “or”applies to some embodiments. Thus, A, B, and/or C can be replaced withA, B, and C written in one sentence and A, B, or C written in anothersentence. A, B, and/or C means that some embodiments can include A andB, some embodiments can include A and C, some embodiments can include Band C, some embodiments can only include A, some embodiments can includeonly B, some embodiments can include only C, and some embodiments caninclude A, B, and C. The term “and/or” is used to avoid unnecessaryredundancy.

While certain example embodiments have been described, these embodimentshave been presented by way of example only and are not intended to limitthe scope of the inventions disclosed herein. Thus, nothing in theforegoing description is intended to imply that any particular feature,characteristic, step, module, or block is necessary or indispensable.Indeed, the novel methods and systems described herein may be embodiedin various other forms; furthermore, various omissions, substitutions,and changes in the form of the methods and systems described herein maybe made without departing from the spirit of the inventions disclosedherein.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to apredetermined desired result. The operations are those requiringphysical manipulations of physical quantities. Usually, though notnecessarily, these quantities take the form of electrical or magneticsignals capable of being stored, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that these and similar terms are tobe associated with the appropriate physical quantities and are merelyconvenient labels applied to these quantities. The present disclosurecan refer to the action and processes of a computing system or similarelectronic computing device that manipulates and transforms datarepresented as physical (electronic) quantities within the computingsystem's registers and memories into other data similarly represented asphysical quantities within the computing system memories or registers orother such information storage systems.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus can be specially constructed for theintended purposes, or it can include a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program can be stored in acomputer-readable storage medium, such as but not limited to, any typeof disk, including floppy disks, optical disks, CD-ROMs,magnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, each coupled to acomputing system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems can be used with programs in accordance with the teachingsherein, or it can prove convenient to construct a more specializedapparatus to perform the method. The structure for various systems willappear as outlined in the description below. In addition, the presentdisclosure is not described with reference to any particular programminglanguage. It will be appreciated that a variety of programming languagescan be used to implement the teachings of the disclosure as describedherein.

The present disclosure can be provided as a computer program product, orsoftware that can include a machine-readable medium having storedthereon instructions, which can be used to program a computing system(or other electronic devices) to perform a process according to thepresent disclosure. A machine-readable medium includes any mechanism forstoring information in a form readable by a machine (e.g., a computer).In some embodiments, a machine-readable (e.g., computer-readable) mediumincludes a machine (e.g., a computer) readable storage medium such asread-only memory (“ROM”), random access memory (“RAM”), magnetic diskstorage media, optical storage media, flash memory components, etc.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific example embodiments thereof. Itwill be evident that various modifications can be made without departingfrom the broader spirit and scope of embodiments of the disclosure asset forth in the following claims. Accordingly, the specification anddrawings are to be regarded in an illustrative rather than a restrictivesense.

What is claimed is:
 1. A method, comprising: generating, by a computingsystem, a dependency tree according to textual news data or a derivativeof the textual news data; determining, by the computing system, a scorecorresponding to a word or phrase associated with or in the textual newsdata according to the dependency tree; and generating, by the computingsystem, a user interface output comprising the score or a derivative ofthe score.
 2. The method of claim 1, wherein the dependency treecomprises a syntactic dependency tree.
 3. The method of claim 1, whereinthe score is a sentiment score.
 4. The method of claim 3, furthercomprising displaying, via a graphical user interface (GUI), thesentiment score.
 5. The method of claim 1, wherein the user interfaceoutput comprises an image.
 6. The method of claim 1, further comprisingstatistical sampling, by the computing system, the textual news data,and wherein the determination of the score is further based on thestatistical sampling of the textual news data.
 7. The method of claim 1,further comprising prior to determining the score, resolving, by thecomputing system, ambiguity in the dependency tree, thereby generating aresolved dependency tree, wherein the determination of the score isbased on the resolved dependency tree.
 8. The method of claim 1, whereinthe method is part of an application framework, wherein the applicationframework provides searchable content to be searched via a userinterface, and wherein the method further comprises deriving searchablecontent from the textual news data.
 9. The method of claim 8, furthercomprising providing, via the user interface, search functions using thedependency tree, the score, or a combination thereof as a basis forqueries.
 10. The method of claim 9, wherein the search functionscomprise queries that are definable by a user identifier, a date,positive sentiment, negative sentiment, news attribution, or anycombination thereof.
 11. The method of claim 1, wherein the textual newsdata and the derivative of the textual news data comprise applicationprogramming interfaces, really simple syndication (RSS) feeds, socialmedia platforms, news aggregators, social news aggregators, or anycombination thereof.
 12. The method of claim 1, further comprisingidentifying, by the computing system, sentiment of or related to atleast a part of the textual news data based on a source of the textualnews data.
 13. The method of claim 1, further comprising identifying, bythe computing system, sentiment of or related to at least a part of thetextual news data based on a date of publication of the textual newsdata.
 14. A method, comprising: constructing, by a computing system, amessage comprising textual news data; generating, by the computingsystem, a dependency tree according to the message; determining, by thecomputing system, a score corresponding to a word or phrase associatedwith or in the message according to the dependency tree; and generating,by the computing system, a user interface output comprising the score ora derivative of the score.
 15. The method of claim 14, wherein thedependency tree comprises a syntactic dependency tree.
 16. The method ofclaim 14, wherein the score is a sentiment score.
 17. The method ofclaim 16, further comprising displaying, via a graphical user interface(GUI), the sentiment score.
 18. The method of claim 14, wherein the userinterface output comprises an image.
 19. A non-transitorycomputer-readable storage medium tangibly encoded withcomputer-executable instructions, that, when executed by a processor ofa computing device the processor performs a method comprising thefollowing operations: constructing, by a computing system, a messagecomprising textual news data; generating, by the computing system, adependency tree according to the message; determining, by the computingsystem, a score corresponding to a word or phrase associated with or inthe message according to the dependency tree; and generating, by thecomputing system, a user interface output comprising the score or aderivative of the score.
 20. The non-transitory computer-readablestorage medium of claim 19, wherein the user interface output comprisesan image, and wherein the method further comprises displaying, via agraphical user interface (GUI), the score.